Added EHMA

pandas_ta/core.py

@@ -1207,6 +1207,11 @@ def hma(self, length=None, offset=None, **kwargs: DictLike):
         result = hma(close=close, length=length, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
+    def ehma(self, length=None, offset=None, **kwargs: DictLike):
+        close = self._get_column(kwargs.pop("close", "close"))
+        result = ehma(close=close, length=length, offset=offset, **kwargs)
+        return self._post_process(result, **kwargs)
+
     def hwma(self, na=None, nb=None, nc=None, offset=None, **kwargs: DictLike):
         close = self._get_column(kwargs.pop("close", "close"))
         result = hwma(close=close, na=na, nb=nb, nc=nc, offset=offset, **kwargs)

pandas_ta/ma.py

@@ -4,6 +4,7 @@
 from pandas_ta.overlap.dema import dema
 from pandas_ta.overlap.ema import ema
 from pandas_ta.overlap.fwma import fwma
+from pandas_ta.overlap.ehma import ehma
 from pandas_ta.overlap.hma import hma
 from pandas_ta.overlap.linreg import linreg
 from pandas_ta.overlap.midpoint import midpoint
@@ -25,7 +26,7 @@ def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series:
     """Simple MA Utility for easier MA selection
 
     Available MAs:
-        dema, ema, fwma, hma, linreg, midpoint, pwma, rma, sinwma, sma, ssf,
+        dema, ema, fwma, hma, ehma, linreg, midpoint, pwma, rma, sinwma, sma, ssf,
         swma, t3, tema, trima, vidya, wma
 
     Examples:
@@ -44,7 +45,7 @@ def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series:
         pd.Series: New feature generated.
     """
     _mas = [
-        "dema", "ema", "fwma", "hma", "linreg", "midpoint", "pwma", "rma",
+        "dema", "ema", "fwma", "hma", "ehma", "linreg", "midpoint", "pwma", "rma",
         "sinwma", "sma", "ssf", "swma", "t3", "tema", "trima", "vidya", "wma"
     ]
     if name is None and source is None:
@@ -57,6 +58,7 @@ def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series:
     if   name == "dema": return dema(source, **kwargs)
     elif name == "fwma": return fwma(source, **kwargs)
     elif name == "hma": return hma(source, **kwargs)
+    elif name == "ehma": return ehma(source, **kwargs)
     elif name == "linreg": return linreg(source, **kwargs)
     elif name == "midpoint": return midpoint(source, **kwargs)
     elif name == "pwma": return pwma(source, **kwargs)

pandas_ta/maps.py

@@ -57,7 +57,7 @@
     # Overlap
     "overlap": [
         "alligator", "alma", "dema", "ema", "fwma", "hilo", "hl2", "hlc3",
-        "hma", "hwma", "ichimoku", "jma", "kama", "linreg", "mama",
+        "hma", "ehma", "hwma", "ichimoku", "jma", "kama", "linreg", "mama",
         "mcgd", "midpoint", "midprice", "ohlc4", "pivots", "pwma", "rma",
         "sinwma", "sma", "smma", "ssf", "ssf3", "supertrend", "swma", "t3",
         "tema", "trima", "vidya", "wcp", "wma", "zlma"

pandas_ta/overlap/__init__.py

@@ -8,6 +8,7 @@
 from .hl2 import hl2
 from .hlc3 import hlc3
 from .hma import hma
+from .ehma import ehma
 from .hwma import hwma
 from .ichimoku import ichimoku
 from .jma import jma
@@ -47,6 +48,7 @@
     "hl2",
     "hlc3",
     "hma",
+    "ehma",
     "hwma",
     "ichimoku",
     "jma",

pandas_ta/overlap/ehma.py

@@ -0,0 +1,62 @@
+# -*- coding: utf-8 -*-
+from numpy import sqrt
+from pandas import Series
+from pandas_ta._typing import DictLike, Int
+from pandas_ta.utils import v_offset, v_pos_default, v_series
+from .ema import ema
+
+
+
+def ehma(
+    close: Series, length: Int = None,
+    offset: Int = None, **kwargs: DictLike
+) -> Series:
+    """Hull Moving Average (EHMA)
+
+    The Hull Exponential Moving Average attempts to reduce or remove lag
+    in moving averages.
+
+    Sources:
+        https://alanhull.com/hull-moving-average
+
+    Args:
+        close (pd.Series): Series of 'close's
+        length (int): It's period. Default: 10
+        offset (int): How many periods to offset the result. Default: 0
+
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value)
+
+    Returns:
+        pd.Series: New feature generated.
+    """
+    # Validate
+    length = v_pos_default(length, 10)
+    close = v_series(close, length + 2)
+
+    if close is None:
+        return
+
+    offset = v_offset(offset)
+
+    # Calculate
+    half_length = int(length / 2)
+    sqrt_length = int(sqrt(length))
+
+    emaf = ema(close=close, length=half_length)
+    emas = ema(close=close, length=length)
+    ehma = ema(close=2 * emaf - emas, length=sqrt_length)
+
+    # Offset
+    if offset != 0:
+        ehma = ehma.shift(offset)
+
+    # Fill
+    if "fillna" in kwargs:
+        ehma.fillna(kwargs["fillna"], inplace=True)
+
+    # Name and Category
+    ehma.name = f"EHMA_{length}"
+    ehma.category = "overlap"
+
+    return ehma

pandas_ta/overlap/zlma.py

@@ -10,6 +10,7 @@
 from .ema import ema
 from .fwma import fwma
 from .hma import hma
+from .ehma import ehma
 from .linreg import linreg
 from .midpoint import midpoint
 from .pwma import pwma
@@ -60,7 +61,7 @@ def zlma(
 
     mamode = v_mamode(mamode, "ema")
     supported_mas = [
-        "dema", "ema", "fwma", "hma", "linreg", "midpoint", "pwma", "rma",
+        "dema", "ema", "fwma", "hma", "ehma", "linreg", "midpoint", "pwma", "rma",
         "sinwma", "sma", "ssf", "swma", "t3", "tema", "trima", "vidya", "wma"
     ]
 

tests/test_indicator_overlap.py

@@ -122,6 +122,12 @@ def test_hma(df):
     assert result.name == "HMA_10"
 
 
+def test_ehma(df):
+    result = ta.ehma(df.close)
+    assert isinstance(result, Series)
+    assert result.name == "EHMA_10"
+
+
 def test_hwma(df):
     result = ta.hwma(df.close)
     assert isinstance(result, Series)